ReviewSensors (Basel, Switzerland)2022
Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Network-Slice-AwareVNF Orchestration for Mission-Critical IoT in 6G Networks: A Multi-Objective Deep Reinforcement Learning Approach.Sensors (Basel, Switzerland) · 2026Article
- Edge AI enabled MIMO MC-CDMA for 6G optimizing spectrum and energy efficiency with SIC and deep reinforcement learning.Scientific reports · 2026Article
- Transformer-assisted hierarchical deep reinforcement learning for energy and spectrum efficient MIMO-MC-CDMA in 6G networks.Scientific reports · 2026Article
- Deep reinforcement learning for resource allocation and scalable numerology in NR-U enabled multi-RAT HetNets.Scientific reports · 2026Article
- A cloud-native framework for seismic waveform data quality assessment: Performance evaluation under equivalent resource constraints.PloS one · 2026Article
- Neural Network-Based Adaptive Resource Allocation for 5G Heterogeneous Ultra-Dense Networks.Sensors (Basel, Switzerland) · 2025Article
- A comprehensive review on internet of things task offloading in multi-access edge computing.Heliyon · 2024Review
- Preference-Aware User Access Control Policy in Internet of Things.Sensors (Basel, Switzerland) · 2023Article
- DeSlice: An Architecture for QoE-Aware and Isolated RAN Slicing.Sensors (Basel, Switzerland) · 2023Article
- Deep Reinforcement Learning-Assisted Optimization for Resource Allocation in Downlink OFDMA Cooperative Systems.Entropy (Basel, Switzerland) · 2023Article
- Two Tier Slicing Resource Allocation Algorithm Based on Deep Reinforcement Learning and Joint Bidding in Wireless Access Networks.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services' demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration-exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.